Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.
Mclaughlin, R., Hipwell, J., Hawkes, D. J., Noble, J. A., Byrne, J. V., & Cox, T. C. (2005). A Comparison of a similarity-based and a feature-based 2-D–3-D registration method for neurointerventional use. IEEE Transactions on Medical Imaging, 24(8), 1058-1066. https://doi.org/10.1109/TMI.2005.852067